1. Field of the Invention
The present invention concerns the field of medical examination apparatuses, and particularly in the field of radiological examination devices.
2. Description of the Prior Art
In the prevention, therapy and after-treatment of oncological illnesses, the search for new metastases is an important radiological object. In a whole-body metastasis examination, modern MR and CT tomography systems generate hundreds of slices images that the radiologist must search through for small lesions. In comparison to PET images in which malignant lesions occur with a very high contrast, such lesions can be relatively inconspicuous in MR and CT images. Due to the enormous quantities of images, for the physician the search is very laborious, tiring and thus also error-prone. In the worst case an error can have fatal consequences for the patient.
In order to minimize the risk of error, usually the so-called four-eyes principle is used. The assessing radiologist (frequently an assistant physician) examines the images, identifies possible occurring metastases and prepares a report. He or she discusses this report later with an experienced colleague (typically a senior physician) who looks at the most important images again. If the assessing radiologist has overlooked an inconspicuous metastasis, however, this may possibly still not be detected even in the second (quicker) check.
In order to minimize the risk of error, in some cases automated expert systems based on error trees are used for diagnostic support in medicine. Such an expert system directs the physician in the manner of a dialog system through a tree of decision points by means of questions and answers about examination results. The physician responds to individual questions (for example with regard to observed symptoms or examination results) and thus supplies the facts. The system then proceeds through the decision tree—from general knowledge to a specific diagnosis. Such a system helps the physician only in the diagnosis, and is strongly dependent on the inputs by the physician and is only conditionally suitable for error prevention. Such a system is known, for example, from DE 101 56 215 and from the corresponding published US application 2003/0092980.
DE 101 51 029 A1 and the corresponding published US application 2005/0065814 A1 describe an expert system in which the selection and order of the medical examination to be effected is automatically established using an expert system, starting from an initial diagnosis.
DE 10 2006 912 015 A1 and the corresponding published US application 2006/243146 A1 describe a system and a method for quantification of a selection property of an image volume which has been acquired by means of a medical imaging modality. An image section (for example a tumor) is thereby selected as representative and the variation of the image (and therewith of the tumor) is observed. Using a decision regulator, the variation can determine a diagnosis or a suggested course of treatment. The decision regulator can access a knowledge-based system (neural network) or a knowledge database.
WO 00/41613 describes a system for decision help in real time in medical treatment which accesses a knowledge-based expert system in order to output rational decisions and recommendations. The system analyzes input data about the health state of a patient and adapts these to rules of the knowledge base in order to arrive at conclusions. The knowledge base thereby also comprises a risk factor module in order to factor in health risks.
U.S. Pat. No. 5,876,746 describes a system in which, for diagnosis or treatment, medical examination results are compared with records of older examination results using a knowledge-based system in order to generate automatic suggestions for the continuative diagnosis. The use of the system is described with imaging examination methods such as CAT, PET, MRI or other radiological methods. The image data are analyzed with a feature extractor in order to make them compatible with rules of the database. A rule comprises a semantic assumption and a conclusion. Further commands can also be generated from the suggestions in order to execute further tests with the system.
Further minimization of the risk exists in optimally high-contrast and high-resolution acquisition methods, but these slower, more expensive and possibly more stressful for the patient.
A need therefore exists for a method and a system which devise the screening more simply and safely for the radiologist.